We analyze the application of ensemble learning to recommender systems on the Netflix Prize dataset. For our analysis we use a set of diverse state-of-the-art collaborative filt...
The concept of diversity was successfully introduced for recommender-systems. By displaying results that are not only similar to a target problem but also diverse among themselves,...
Recommender Systems belong to a class of systems intended to assist individuals make evaluations about entities in meaningful ways. In this paper we discuss the issues in the desi...
Recommender systems are intelligent E-commerce applications that assist users in a decision-making process by offering personalized product recommendations during an interaction s...
Recommender systems are changing from novelties used by a few E-commerce sites, to serious business tools that are re-shaping the world of E-commerce. Many of the largest commerce...
In this paper we present a recommender system design for recipe based on-line food shopping. Our system differs in two major ways from existing system. First we use an editor that...
Martin Svensson, Jarmo Laaksolahti, Kristina H&oum...
Automated collaborative filtering (ACF) systems predict a person’s affinity for items or information by connecting that person’s recorded interests with the recorded interests...
Jonathan L. Herlocker, Joseph A. Konstan, John Rie...
Abstract. This paper focuses on the utilization of the history of navigation within recommender systems. It aims at designing a collaborative recommender based on Markov models rel...
Providing justification to a recommendation gives credibility to a recommender system. Some recommender systems (Amazon.com etc.) try to explain their recommendations, in an eff...
Recommender systems are used to suggest customized products to users. Most recommender algorithms create collaborative models by taking advantage of web user profiles. In the las...
Elica Campochiaro, Riccardo Casatta, Paolo Cremone...